UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification
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Summary:
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.
Series:
Working Paper No. 2020/262
Subject:
Early warning systems Economic and financial statistics Financial crises Financial statistics Machine learning Technology
Frequency:
regular
English
Publication Date:
November 25, 2020
ISBN/ISSN:
9781513561660/1018-5941
Stock No:
WPIEA2020262
Pages:
24
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